Effective input variable selection for function approximation

Authors:
L. J. Herrera;H. Pomares;I. Rojas;M. Verleysen;A. Guilén
Affiliations:
Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Machine Learning Group, Louvain la Neuve, Belgium;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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Abstract

Input variable selection is a key preprocess step in any I/O modelling problem. Normally, better generalization performance is obtained when unneeded parameters coming from irrelevant or redundant variables are eliminated. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Nevertheless, for continuous variables, it is usually a more difficult task to determine the mutual information between the input variables and the output variable than for classification problems. This paper presents a modified approach for variable selection for continuous variables adapted from a previous approach for classification problems, making use of a mutual information estimator based on the k-nearest neighbors.